Do you own or manage a business? Managing a company involves many steps. These steps begin right from the genesis of an idea through its execution. They also involve choosing a development procedure and managing risks. Most software companies these days are adopting the latest project methodologies, like DevOps and agile. After all, you have to keep up with changing trends and implement them in your own life—but you also have to manage the risks associated with information technology (IT) and tackle them to manage your business. So, you’re probably wondering, what is IT risk management?

That’s what we’ll be talking about today. In this post, I’ll guide you on what constitutes risk in IT. I’ll also explain why you need to understand risk in a business atmosphere. After that, we’ll examine the different steps involved in IT risk management. And finally, we’ll discuss how you can minimize risk at the workplace. So why wait? Let’s kick-start this guide.

What Is Risk Management?

The first step in learning about risk management is understanding what “risk” means. “Risk” refers to any kind of threat to IT management. Management issues, human errors, accidents, and financial and legal issues are examples of risks. For instance, a hardware issue that wipes out a huge amount of data is a risk. The influence of risk can stretch to not one but a variety of mediums in the work environment. Hence, we need to know how to manage risk in IT.

Risk management is the control and management of threats in the workplace. There is a set of processes that involves analyzing and assessing different types of risks. A risk analyst’s job is to bring out positive solutions that may reduce the chance that a risk occurs. Apart from that, risk management includes complying with new regulations as trends change. But why is risk management important? Let’s take a look at the next section.

Why Do We Need Risk Management in IT?

Risks can come from external as well as internal sources. As I noted above, a hardware failure causing data loss can be a risk. Similarly, data theft by hackers or malware is another risk. Hence, it is crucial to manage these risks. The threats include financial and logistical issues, human errors, and more. If these threats surround your company, you risk losing money or your reputation.

Risk management helps you save money and resources. If you identify, plan, and execute solutions, the number of risks can decrease. So, you must include risk management as one of your company’s key objectives. This will save time for everyone in the company. You may wonder if it’s important to tackle every risk that comes your way. If you don’t, the risks can impact your employees as well.

Can you control risks in your company? In the next section, I’ll guide you on the various steps involved in risk management. If you follow them step by step, you can expect fewer threats to your company. The steps will also help you produce more solutions. So, let’s proceed with a detailed guide that will help you control risks.

What Are the Risk Management Steps in an IT Company?

How can you manage risks in IT? Risk management is an ongoing cycle. It includes the key elements of identifying, testing, and executing to fulfill it. If you find solutions to risks, you can remove them from your organization.

Risk management involves six steps that are important for every IT company.

Identify the Scope of the Problem

Firstly, map out the necessary outline to follow before starting the process. This outline should include the objectives of the process along with the employees’ goals. Once you’ve done that, move on to the next step: identifying the problem.

Identify the Risk

First, ask yourself what can go wrong. If there are any answers, those are the potential risks. You have to identify them to decide how to remove them in a situation. The main source of the problem in an event is your biggest risk. If you can’t identify the risk here, it will harm you later.

For instance, suppose you store your data in a data center. In that case, what’s the biggest risk? Obviously, the answer is data security, because poor security measures will lead to data theft.

Now that we’ve identified the risk, let’s move on to the next step.

Analyze the Risk

Can a risk affect us? This is the first question that comes up when you identify risks in IT. Risks are assessed based on their frequency, but you can measure them based on loss as well. You can make a good guess whether a risk is harmful or not. This will impact your company accordingly. Therefore, you need to be experienced to make these guesses. Risk evaluation can be tough because data isn’t easily available. It can also be based on asset evaluation. But if you don’t try to test your guess, you’ll never learn. IT companies rely on risk analysts for this step. We’ll discuss how to control the risk in the next step.

Treat the Risk

What should you do when you see a risk? You can either eliminate the risk or control it. You must understand how to treat risks. Based on your decision, make a reusable plan to consider the next time similar risks occur. If the risk causes loss, remove the risk or control the loss to avoid any threats. Making a smart plan is key to treating risks in the workplace. For instance, let’s explore the data center security issue we discussed earlier. How can we control the risk of data security? Advanced security like restricted-access CCTV monitoring is the answer.

Is that all you need to manage risks? Well, unfortunately not. In the next steps, we’ll discuss how to safeguard your plan.

Execute the Plan

Apply the steps you’ve planned to check for positive results. If you’re unable to mitigate the risk with your plan, repeat the first four steps and create a new plan. Risk management plans can have a few errors before success. Ensure that you don’t sacrifice the company’s goal while making your plan. If the plan works out well, you can move on to the last step of risk management.

Review the Plan

You can expect to face failures in risk management. Not every plan will be perfect. Hence, you should research effective control strategies and add them to your plan. This will help to make the plan better for the next time you find a threat to your company.

As I mentioned earlier, the steps to risk management are important to achieve a company’s goal. Business environments continue to change. You have to stay up to date with the trends to find strategies for effective risk management.

But, what strategies can you apply to manage risk? We’ll discuss them in the next and final section of this post.

Strategies to Minimize Risks in IT

An IT company will need different ways to manage risks. Most of these strategies include a good work culture. If the people in your company are happy, you can minimize risks. Let’s discuss the strategies you need to follow to manage or reduce risk.

Lead by Example

Safety measures aren’t just learned but demonstrated. You should promote a safe environment for all employees. With the advent of any threat, the team will be better prepared to face the risk. What kinds of threats might you face? Well, that varies from company to company. All in all, you should establish protocols so that all employees can prepare themselves. Not only that, arrange training sessions for your employees to make them aware of safety protocols and ensure that they follow the rules and protocols.

Bond With Your Employees

Do you get along with your employees and coworkers? If the answer is no, you should work on improving relations. Employee bonding is important in a company. It builds strong relations, support, and loyalty in the workplace. You can manage risks better if everyone works together. The time it takes to tackle a risk will lessen. Not only that, if you’re friendly with your employees, they’ll be more productive. You can also try to make employees happy by showing the value of their work from time to time. Risk management becomes easier when everyone feels like part of the company.

Keep the Workplace Healthy

Does everyone at work stay healthy? Do all employees have healthy habits? A healthy work culture promotes productivity. So, check up on everyone and see if they’re happy. If someone is unhappy with their job, sit with them and listen to their problems. You can tackle the risks in a company only when everyone is willing to work for the company. After all, health is wealth!

Make Smart Decisions

Regardless of whether a risk is virtual or physical, you need to act quickly in response and avoid prolonging a threat. If there’s a loss of data, look for backup. If someone is hurt, call for medical help. Managing risks in a company is important for growth. The best way to manage risks in IT is by making smart decisions that benefit everyone.

What’s Your Role in Risk Management?

Ultimately, your role is to be aware of threats from the start. IT companies should have many solutions for risks. You must discover the risks and remove them. You can reduce them by using the correct techniques. In the big picture, your losses can cost your company. Good risk management is all about minimizing costs. Learn how to manage risks, and you can turn the losses into profits.

This post was written by Arnab Roy Chowdhury. Arnab is a UI developer by profession and a blogging enthusiast. He has strong expertise in the latest UI/UX trends, project methodologies, testing, and scripting.

Measuring the maturity of your DevOps team might sound difficult, but it isn’t at all. Simple key performance indicators (KPIs), such as the deployment success rate or mean time between failure, give a good indication of the maturity of your DevOps team. By “mature,” I mean that your team consistently and smoothly operates at a high level and can deploy several times a day with very little risk.

This article will answer these questions:

• What’s a mature DevOps team really like?
• How does a new DevOps team organize itself?
• What are the phases of DevOps maturity?
• What metrics should a DevOps team measure?

Let’s get started.

Elements That Define DevOps Maturity

It’s possible to measure DevOps maturity in many ways. Maturity includes cultural, technical, and process-related elements. And you can measure all of these through DevOps KPIs or metrics.

For example, on a cultural level, you’ll want to learn how DevOps engineers share knowledge among team members. An active environment of knowledge sharing is a good sign that your team works well together.

On a technical level, you’ll want to measure DevOps KPIs related to errors, mean time to repair a bug, or the availability of a service. It’s much easier to measure these technical metrics than it is to measure cultural elements.

Last, let’s have a look at process metrics. These are mostly concerned with measuring the time needed for completing certain tasks, such as spinning up a new instance of a service. How fast can a DevOps engineer do this? For a recently formed DevOps team, the required time to accomplish such a task might be much higher than it is for a mature team. That’s because an immature team is often still working on standardizing and optimizing processes.

Next, let’s discuss the different phases of maturity for a DevOps team.

Phases of DevOps Maturity

Tuckman’s stages of group development accurately describes what a newly formed DevOps team experiences. When a new team gets together, all team members go through the following four phases:

• Forming
• Storming
• Norming
• Performing

How do these phases translate to a DevOps team? Let’s find out.

Forming: Need for Leadership

During the forming phase, there’s a lack of clarity, which means the team needs a leader who can provide guidance and strategy. Often during this stage, you either won’t find any DevOps implementation or there’ll be a bare minimum.

During this phase, the team starts to explore possible ways of test automation and different tools for implementing continuous integration (CI). At this stage, DevOps engineers write simple scripts that help automate repeating tasks. It’s safe to say you’ll find an immature team at this stage.

Storming: Fundamental Progress

Next up is the forming stage, which matters because it reveals the team’s initial progress. The team members try to establish fundamentals, such as implementing a simple CI flow with integrated test automation.

At this stage, developers can push their code to the continuous integration (CI) pipeline and receive valuable feedback about it. Often at this stage, you’ll find a strong change toward DevOps culture. This means that basic DevOps tools such as a CI pipeline have been implemented with some basic test automation. Still, you won’t find much of a focus on defining KPIs because the DevOps team is still in the process of building a strong DevOps tooling baseline.

Norming: Independence and Shared Ownership

Next, the norming phase brings the team clear responsibilities and direction. It’s possible to delegate smaller decisions to team members. A change toward agile management of the team happens because the team no longer needs to consult its leader for every decision. Strong independence and a feeling of shared ownership by the team often emerge at this stage.

The norming phase is a time of strong automation—from building the code to testing the automation and code deployments. At this stage, you’ll often find a happy development team whose members are able to improve their efficiency through the integrated toolchain. Because of this, the team can establish continuous delivery.

Moreover, the DevOps team also implements monitoring as part of this phase. Through monitoring, team members can set different KPIs to measure the health of the DevOps team as well as its code and deployments.

Performing: Time for Fine-Tuning

Finally, we’ve arrived at the performing phase. A successful implementation of DevOps is a hallmark of this phase.

During this phase, you’ll find room for experimenting. The team finds optimizations through experimentation. Standardized processes have been established, and there’s an active atmosphere of knowledge sharing among team members.

A team that’s in the performing phase focuses on improving important metrics, such as availability or reducing the error rate.

Now that you understand the different phases of DevOps maturity, what are some ways you can measure that maturity?

7 DevOps Metrics You Should Measure

Let’s take a look at seven of the most important DevOps KPIs for measuring a team’s maturity.

MTTF: Mean Time to Failure

First of all, MTTF refers to the time until the next failure. For example, how often does your deployment cause a failure? Ideally, you’ll want the MTTF metric to be as low as possible. A high MTTF rate can indicate problems with the quality of your software. For example, you may not have enough tests covering different scenarios that might contain bugs.

MTTD: Mean Time to Detect

Next, MTTD is an important KPI for a DevOps team. It tells the team how long it takes before they detect an issue. Immature teams require quite some time to detect issues because they have no monitoring implemented. This means it’s much harder for an immature DevOps team to replay events leading up to an issue. They don’t have any data to fall back on.

In contrast, a more mature team that has monitoring implemented can detect issues faster through the data that team members capture, such as logs or performance data.

MTTR: Mean Time to Repair

MTTR refers to the time needed to fix an issue or error. An immature team might not have much experience and knowledge on the system, which means they’ll likely end up with a high average time.

However, a team at the performing stage won’t need much time to repair incidents. Why? That team has already gathered a lot of knowledge about the DevOps implementation and has been actively sharing knowledge about common incidents. It’s very likely the team has a ready-made solution to the problem.

MTBF: Mean Time Between Failures

The MTBF metric is the most straightforward one. It refers to the average time it takes for a component to fail. This metric is especially useful to determine how stable a particular component in your codebase is. If a particular component fails relatively quickly compared with other components, then you might want to validate the code or architecture for this component. Obviously, the goal is to have components that rarely fail!

Deployment Success Rate

Next, the deployment success rate calculates the rate of successful and unsuccessful deployments. This success rate should be as high as possible for mature teams.

You can improve the deployment success rate by automating and standardizing the deployment process. A higher deployment success ratio will reduce frustrations among team members and create a less stressful job experience.

Deployment Frequency

A high deployment frequency can be an indicator of an optimized CI pipeline. In addition, the ability to deploy frequently allows for a more agile approach toward the development team. Mature DevOps teams often have a high deployment frequency because they have their processes streamlined and standardized.

Next, let’s take a look at the importance of measuring the error rate.

Error Rate

Last, the error rate tells the DevOps team how often new bugs pop up in running applications. It’s important to capture spikes in the error rate because these can indicate that something isn’t right. For example, there might be a database that’s being overloaded with SQL requests, and the DevOps infrastructure isn’t able to scale as quickly as needed.

Here, log analysis can help the team detect such error spikes. Moreover, log analysis lets you measure the number of error log messages.

(Do you want to learn more about log analysis? Check out XPLG’s blog about log forensics.)

Maturity Through DevOps Metrics

In short, metrics are an important instrument to measure the maturity of your DevOps team. Make sure you understand the different phases of team formation through Tuckman’s model. It allows you to better understand how the team functions in each stage.

When measuring metrics, try to start simply, with metrics such as the deployment success rate or mean time to failure. These metrics give important intel about the stability of your DevOps implementation.

Good luck with your DevOps journey!

This post was written by Michiel Mulders. Michiel is a passionate blockchain developer who loves writing technical content. Besides that, he loves learning about marketing, UX psychology, and entrepreneurship. When he’s not writing, he’s probably enjoying a Belgian beer!

If you work in IT, especially on the decision-making side, then you should be aware of how vital logging is. When things go wrong with software in production, a solid logging approach often is the difference between hours of fruitless troubleshooting and an easy fix. Today, we’re here to talk about logging by covering a specific angle. Namely, we’ll answer the question, “What is log collection?”

Why is this an important question to answer? As it turns out, a typical, modern tech organization will have many types of logs, written to an equally large number of targets, or destinations. We’re here to argue that, although there’s certainly a reason and a place for each of these log types, you can also find benefits in unifying them. That’s what this post is about.

We’ll start with the basics. Before getting into log collection, we need to be on the same page regarding logging itself, so that’s where we’ll begin: by defining logging, explaining why this is such a big deal, and covering the benefits it provides. Then, we’ll proceed to cover the variety one can find in the logging world. We’ll cover the types of logs, explaining the reasons for each one.

Then, at last, we’ll get to the meat of the article. By answering the title question, we’ll explain why there is value to be found in unifying your various log sources. We part ways with some final considerations and tips on tools you can use to unify your logging approach.

Let’s get started.

Log Collection Basics

As promised, we’ll start with some definitions. The first one of them is a definition of logging itself. Feel free to skip ahead if you’re already experienced or are at least familiar with logging.

What Is Logging?

So, what is logging? In short, logging is the practice of creating and maintaining logs. A log is a recording of an event of interest that happened in a computer system or application.

It’s common to write log entries to text files on the disk, but that’s not the only available option. It just happens to be one of the most popular ones. As you’ll see later on this post, you can write logs to a variety of destinations, such as database tables, email, cloud services, and many more.

Since we’ve just covered the “what,” it’s time to get to the “why.” Why do people bother logging? What do they expect to get from it?

We Use Logging Because Tech Is Complex

Why bother logging? The TL;DR version is tech is complicated and things break often.

Now, for the slightly longer answer.

IT is complex. Let’s take software development as an example.

Developers write code, along with unit tests, to verify its behavior. When they’re done, they check in their changes to a CI server. The CI server will then build the application, resolving whatever dependencies that code requires, besides running the unit tests and maybe additional types of automated tests.

Depending on the type and size of the organization, the code might be handed to a QA department, which will then submit the application to myriad types of tests, both manual and automated. When they’re satisfied, the application might still need to go through an acceptance phase, in which a stakeholder gives their sign-off.

Even with all those verifications and tests done at each stage, there’s still no guarantee the application will work as intended when it gets to the production environment. When things do go wrong, the programmers assigned to fix the problem will need to know, among other things,

• The exact date and time the problem occurred.
• In which part of the application the problem happened.
• Details about the user session in the application.
• The stack trace of the error or exception that occurred.

And what would be a sure way to get these pieces of information where they need to be? You’ve got it: logging.

Logs in All Shapes and Sizes

In order to understand why it might make sense to unify your logs from various sources, you must first understand what all those sources we keep telling you about are. That’s what this section covers.

We’ll start by covering some of the different types of logs an IT organization might employ. After that, you’ll see some of the most often used destinations (or targets) for logs.

Types of Logs

What follows is a list of existing types of logs. It’s not an exhaustive list, but it features the most common types of logs you’re likely to find at your company.

Application Logs

Application logs are simply logs that are generated by the various applications you happen to use. Their main purpose is to allow maintenance programmers to perform an after-the-fact debugging session of the application. It’s sort of like a time machine: by reading through the log entries generated by the application, the developers can understand how it behaved in production and, hopefully, detect and fix whatever is causing it to malfunction.

Detecting errors isn’t the only thing application logs are good for, however. Logging can also help you answer questions that are of interest to the business, such as:

• What are the most and least used features?
• How many visitors does each page get per day (and other time periods)?
• How much time do visitors spend on each page or feature?

In short: application logs are useful to detect and fix problems and also to help the organization gather data and insights that can be useful to improve the application.

System Logs

System logs are another important type of log. Unlike application logs, system logs are generated by the operating system itself—hence the name.

Most entries you’ll find in the system log are written by the operating system itself, but applications can also add log entries there.

On Windows, the system logs are called the event log, which you can view through the Event Viewer application. If you’re using Linux, the system log consists of the Syslog service and its successors.

Finally, if you’re a macOSX user, you can access the system logs by using the Console app.

Web and Application Server Logs

The final log type we’re covering today is the log generated by an application or web server.

Web servers such as Apache, IIS, and NGINX generate log files that are essential to troubleshoot problems with websites or web applications, and the same is true for application servers.

Common Logging Destinations

You’ve just read about three of the most common types of logs. Now you’ll learn about some of the most common destinations or targets for logs.

Text Files

It’s very common to write logs to plain text files. Text files are very approachable. They’re cheap, easy to use, and ubiquitous. You don’t need fancy software to read them; a common text editor will do. Except for nuisances such as differences in end-of-line characters between Windows and *nix systems, text files are intrinsically cross-platform.

Database

Database tables are another common destination for logs. The main justification for picking a database as a target for logging would be the ease of querying and the other relational facilities, such as relational integrity.

Email

Emails are yet another interesting target for a log. By redirecting the log to a specialized email address, you could, for instance, automate the opening of tickets when a problem happens in production.

Cloud Services

An increasingly popular alternative is to integrate logging targets with cloud solutions. For instance, you could have a logging target that writes log messages to providers such as Amazon CloudWatch Logs, Azure Storage, or Google Logging.

Log Collection; AKA Why Unifying Your Logging Is Worth It

As you’ve seen so far, logging can take many forms. Logs vary in their types, formats, and destinations. They vary in their severity levels, intended target audience, and in how often they get written.

When you take all of that variety into account, it doesn’t take long until you realize that a comprehensive logging approach can be an incredible asset for the business. Analyzing a large number of log entries can be a powerful way of obtaining insights about pretty much all facets of an IT operation.

However, all of those precious insights might be out of your reach if your logs are all scattered through a thousand different places.

That’s when log collection comes in handy. By having an automated process that collects logs from varied sources and aggregates them into one place, you have the best of both worlds, so to speak. For starters, you don’t need to change anything to the way you currently do logging. In other words, it’s the log collection process that will adapt to your already existing logging approach and not the other way around.

And then you get all of the benefits of having your logs concentrated in a single place. Having all logs from the organization at your fingertips will allow you to obtain insights that wouldn’t be possible otherwise.

You could, for instance, analyze web server logs along with application logging, to determine whether there’s a correlation between the server’s performance and the time users spend on the application. That’s just an example, but the potential is limitless.

What Now?

Let’s do a quick summary of what we’ve seen today.

We’ve learned what logging is, why it’s important, and the benefits it brings to an organization. You’ve learned that logs come in many shapes and sizes, different by their types and destinations, among other properties.

Getting all these myriad types of logs together in one place gives you the ability to obtain insights that wouldn’t be possible to get otherwise. That process is what we call “log collection,” and today you’ve learned why it is valuable. Where to go now?

First, don’t stop learning. Continue reading articles, on this blog and elsewhere, to learn about logging and related topics. Also, research and learn about tools at your disposal. For instance, take a look at XpoLog’s offering, which is a full-fledged log management tool, powered by AI and completely automated.

Thanks for reading, and see you next time.

Before the need for log correlation, there was a time not so long ago when reading software application logs was simple. Your application would output log files in sequential order, and you’d read through them. In the event of a bug, software outage, or security incident, you could easily parse what happened and when. It was a tedious process, but it was simple.

That’s not the case any more. In the interest of scaling our software to service millions of users, that software is significantly more complicated. In today’s software landscape, logs originate from dozens of sources. Even a relatively simple web application has client logs, logs from a load balancer, logs from the web server, database logs, and logs from worker services that handle longer-running tasks. If your application has even slightly more advanced capabilities, your logging requirements expand considerably. Every new feature requires more logging in order to monitor correctly.

So how do we handle all these logging demands? That’s where log correlation comes in. What is log correlation, you ask? Keep reading. In this post, we’ll define log correlation and talk about why it’s so important.

Correlate any log data, hands-free, and turn it into actionable insights in minutes – download XpoLog free

When Something Goes Wrong, Challenges Arise

One of the negative side effects of this level of complexity is that troubleshooting issues is complicated. Few bugs are simple, but complicated architectures mean that even pinpointing a bug is time-consuming.

As user activities propagate between different parts of the system, they generate log events. As an engineer, you can track down those log events, but it’s going to take some time. On busy systems, there’s an extra layer of complication. Similar application actions will generate similar log entries. Do you know if that database log entry came from the bugged request, or a similar one that completed successfully?

Sometimes, the challenge can come from just finding the logs. Where does your web server log to? What about your database? Managing logs in modern applications can feel like trying to solve a maze in the dark when you don’t know if there’s an exit.

This is the complexity we’re talking about. Eventually, you’ll figure out where each log entry came from, and you’ll be able to sort out the root cause of your bug. That’s good! But it’s likely that it’s going to take you hours, and when there’s a production bug, your bosses aren’t likely to be excited by hours before you even know what the bug is.

This delay is even more significant when dealing with a suspected security event. When you think someone has compromised your system, a few hours can be the difference between repelling them and a major security breach.

Discover errors and possible security risks in your entire environment automatically – download XpoLog and see how easy it is.

Different Services Log Differently

Another significant complication stems from the fact that different services have different log formats. In the heat of the moment, you want someone who’s an expert on the service experiencing the bug. But reading Apache logs is a lot different from reading log output from Postgres. Someone who’s an expert in Apache might have to guess what’s happening with a log from another application. This amplifies the difficulty of tracing an event through your system’s logs. Each level of communication overhead introduces another place where your process can fail. Each failure pushes back the time necessary to resolve your problem.

Log Correlation Tackles Those Challenges

Thankfully, you don’t have to live with all of this complexity. Log correlation software, like Log Management from XPLG brings all of your logs into a single location. By moving all logs to a centralized location, a significant element of complexity is removed. When something goes wrong, you know right where to look.

But log correlation software isn’t simply the act of bringing all logs into a singular location. While that’s helpful, log correlation is much more powerful than just that.

Tying the Threads Together

Instead, log correlation fills a much more valuable purpose. It’s able to track actions throughout your system and trace the logs they generate. That’s the “correlate” part of log correlation. Under the hood, log correlation is a terrific bit of engineering. Application programmers build pattern-matching software which is able to direct the software to determine which parts of disparate logs represent the same action. Quality log correlation software comes with these rules built in by default. It’ll also let your team define custom rules. All that hard-earned knowledge about how to trace an action through your system is fed directly into the system. Instead of hours spent tracking actions through the system, the same logic is executed in milliseconds.

Working Automatically With Different Systems

One significant benefit of software like XPLG is that it features a robust application store where you can add log plugins based on the services you use. Because these parsing tools are written by experts in their technology, picking up one of these plugins is like adding that expert right to your debugging team. Now, you eliminate that communication overhead. Your team doesn’t need to constantly talk back and forth to figure out where an event originated. The logs are effectively sequenced in order from the start of an event to the end. You’re able to quickly and smoothly determine how a bug traced through your system, and root out the cause in minutes.

What’s more, slight configuration differences in systems can cause big problems in log collection. An effective log correlation service will help smooth over those bumps so you’re getting the right data, every time.

The Next Level: Log Analysis

When you run an application that serves millions of users, waiting for a bug or security event to announce itself means you’re behind. Especially with the case of security events. As we noted before, every minute lost when an intruder is trying to compromise your system is the potential for real trouble.

Instead, log correlation and management systems can alert you when unexpected behavior is happening, before a user even knows something is wrong. You’re able to see the overall health of your system in real time. For instance, imagine that a network link between your web server and database is severed. In a traditional logging environment, you don’t know about this problem for some time. You first need to receive reports that requests are failing. Then you need to begin tracing each of those events through disparate system logs. It won’t be until you’ve done two or three that your team will suspect there’s a network outage issue.

Instead, with an effective log correlation system, you’ll be able to recognize that a high percentage of requests are failing to reach the database in just minutes. The time saved turns an outage of hours into one that takes minutes to resolve. Log correlation systems can even provide configuration options to automatically notify team members when an unexpected number of errors occur. It could be that the first time you learn your network is down isn’t from a customer, but from an automated system. You can fix the problem before someone even knows something is wrong.

Log Correlation Lets You Focus on What’s Important

In today’s software world, complexity is everywhere. It grows constantly, and it’s nearly impossible to avoid. Every new feature request means adding a new layer of complexity to your software. Most of the time, this doesn’t impact your team. When things are running well, complexity isn’t a source of stress. But when things break, that complexity can feel back-breaking. Log correlation is a tool to reduce the weight of that complexity. It provides real ways to simplify how you visualize data flowing through your systems. When implemented correctly, it even helps your team take action proactively. Best of all, XPLG makes it easy to start, for free. What are you waiting for?

“What is MTTF?” That’s the question we’ll answer with today’s post. Yep, the article’s title makes it evident that the acronym stands for “mean time to failure.” But that, on its own, doesn’t say anything. What does “mean time to failure” actually mean? Why should you care? That’s what today’s post covers in detail.

The structure of this post will mostly follow the template we’ve laid out with the mean time to detect (MTTD) article. We’ll start with the “what” of MTTF, giving a complete definition of the term. After that, we’re ready for the “why”—you’ll learn why you and your organization should care about this metric, understanding all the benefits it can provide. After that, we’ll finally be ready for some practical tips. We’ll invite you to roll-up your sleeves and learn how to calculate MTTF.

Before parting ways, we list other essential DevOps metrics you should also know. Let’s get started.

Improve uptime by 35% – download XpoLog and get ML-powered insights in real-time about errors, anomalies, exceptions, and more.

Defining Failure

It’s common for me to start posts by offering a definition of its subject matter. This time I’m taking a different route, though: let’s begin by defining “failure.”

You might think that failure is such an obvious concept that it bears no definition. After all, if something doesn’t work at all, it has failed. What’s so complicated about it?

I beg to differ. Things aren’t black and white when it comes to failure, especially in the IT world. Indeed, there can be more granular modes of failure.

Is a car with a flat tire a failure? What about a phone whose touch screen features randomly don’t work? And a musical keyboard which doesn’t produce sound in some keys? Are these examples failures or not? The way I see it, yes. Even though you could say that they “work,” they don’t work at the level they’re supposed to.

A failure, generally speaking, means that something doesn’t meet its goals. Specifically, in the tech world, that usually means a system outage, aka downtime. But there can be scenarios in which, despite not having a full-blown system outage, you can say that there is a failure. You could have an application that performs orders of magnitude slower than it should. That’s failure. It doesn’t matter that the result was technically correct when the system takes more than 24 hours to perform a task that should have taken a few minutes, at the most.

What Is MTTF: A Definition

MTTF is a critical KPI (key performance indicator) for DevOps. As you already know, the acronym stands for mean time to failure. But what does that actually mean? In a nutshell, MTTF refers to the average lifespan of a given item. In other words, it refers to how long a piece of technology is supposed to last operating in production.

MTTF is closely related to another metric—MTBF (mean time between failures.) Well, to be fair, they’re virtually the same thing, with just one important difference. You’d use MTBF for items you can fix and put to use again. On the other hand, you’d use MTTF for items that can’t be repaired. If you find yourself in such a scenario where MTTF is used as a metric, that means repairing the problematic item isn’t an option, so you’ll have to replace them.

Mean time to failure sets an expectation. It represents the length of time you can expect an item to work in production. Keep in mind that when companies calculate the mean time for failure for their various products, they don’t usually put one unit to work continuously until it fails. Rather, this metric is often computed by running a huge number of units for a specific amount of time.

Why Should You Care About MTTF

You’ve just learned the “what” of mean time to failure. Let’s now turn our focus to the motivations behind calculating this metric. Like MTTD, one of the best reasons for calculating MTTF is to improve it. Unlike MTTD, though, this metric improves when it goes up instead of down.

MTTF measures reliability. With it, you can know how long a product typically works before it stops working. Having this piece of data, your organization is able to make informed decisions on important issues, such as inventory management (which even includes from which brands to purchase or not purchase), scheduling of maintenance sessions, and more.

MTTF also helps us, albeit indirectly, to evaluate your monitoring mechanisms. This is a quote from our post on MTTD:

“MTTD also has an additional—and arguably more important—benefit: it serves as a test of your monitoring mechanisms. Here’s the thing—your organization already adopts tools and processes to monitor incidents. If these tools and processes work as intended, it shouldn’t be that hard to keep your organization’s MTTD low. The opposite is also true. If you adopt incident management mechanisms that aren’t up to the task, you and your DevOps team will have a hard time keeping MTTD down, which can result in catastrophic consequences for your organization.”

You could say that MTTF, as a metric, relies on MTTD. In order to track how much time components work until they stop, the organization must be able to detect system outages and other problems. So, by carefully tracking MTTF, you’re also keeping an eye on the health of your monitoring procedures.

Monitor your systems, services, and infrastructure better – download XpoLog free.

Calculating MTTF

You calculate MTTF taking the total amount of hours of operation (aka uptime) and divide it by the number of items you’re tracking.

Suppose we have four pieces of equipment we’re testing. The first one failed after eleven hours, while the second one failed after nine hours. The third failed after seven hours, and finally, the last one failed at five hours. So, we have a total uptime of 32 hours, which divided by four equals eight hours.

This suggests this particular equipment will need to be replaced, on average, every eight hours.

Other Metrics to Be Aware Of

MTTF is a key indicator to track the reliability of your assets. Here are some other important metrics you should probably know:

• MTTD (mean time to detect): The average amount of time it takes to detect problems in the organization.
• MTTR (mean time to repair): The time it takes to fix an issue after its detected.
• MTBF (mean time between failures): The time the organization goes without a system outage or other issues.

What’s Next?

In this post, we’ve answered the question, “What is MTTF?” Mean time to failure is an important metric you can use to measure the reliability of your assets. Since MTTF shows the amount of time a product, component, or other types of assets usually work until they fail, you want to keep it as high as possible.

When it comes to DevOps, MTTF is one of many important metrics we need to track. Just like MTTD, the previous metric we’ve covered, MTTF serves more than one purpose. The first and obvious one is to be a reliability measure. By tracking the mean time to failure, we understand how reliable our equipment, components, and assets are, so we can make more educated decisions. But MTTF can also help us to evaluate the effectiveness of our monitoring solutions because we have to detect outages in order to measure the time between them.

Where to go now? Well, keep searching for more knowledge. Learn about other important metrics. Learn about tools that can help you with such metrics.

For instance, take a look at the fully automated log management tool XpoLog. It collects, parses, and tags logs from many different sources.

XpoLogs’ ML-powered engine adds layers of intelligence over your searches, it automatically and proactively detects errors and allows you to prevent outages and meltdowns. XpoLog contains a leading analysis apps marketplace with thousands of ready-to-use-reports and dashboards, to extract actionable insights immediately, in real-time. Download XpoLog now and improve your monitoring mechanism.

Log management is essential for tracking metrics such as MTTD and MTTF since logs are very reliable sources of information when it comes to system outages.

That’s it for today. Thanks for reading.